{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "168a2028",
   "metadata": {},
   "source": [
    "# THE CODE IS IN MY GITHUB!!!\n",
    "Go and checkout my github if you want the **to_candle** file!\n",
    "## https://github.com/kecoma1/Trading_BOT\n",
    "https://github.com/kecoma1/Trading_BOT/tree/main/ta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "45bdbc0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import to_candle"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8ce9327c",
   "metadata": {},
   "source": [
    "### Metatrader dataframe\n",
    "The dataframe that metatrader metatrader provides with the csv file may not be the one we want. This is because MT5 gives us all the ticks of the price (which is wonderful)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7b38487f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>&lt;DATE&gt;</th>\n",
       "      <th>&lt;TIME&gt;</th>\n",
       "      <th>&lt;BID&gt;</th>\n",
       "      <th>&lt;ASK&gt;</th>\n",
       "      <th>&lt;LAST&gt;</th>\n",
       "      <th>&lt;VOLUME&gt;</th>\n",
       "      <th>&lt;FLAGS&gt;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021.09.30</td>\n",
       "      <td>00:00:00.204</td>\n",
       "      <td>1.15982</td>\n",
       "      <td>1.15985</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021.09.30</td>\n",
       "      <td>00:00:00.409</td>\n",
       "      <td>1.15939</td>\n",
       "      <td>1.16015</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2021.09.30</td>\n",
       "      <td>00:00:00.672</td>\n",
       "      <td>1.15984</td>\n",
       "      <td>1.15987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021.09.30</td>\n",
       "      <td>00:00:26.007</td>\n",
       "      <td>1.15978</td>\n",
       "      <td>1.15981</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021.09.30</td>\n",
       "      <td>00:00:27.707</td>\n",
       "      <td>1.15984</td>\n",
       "      <td>1.15987</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2021.09.30</td>\n",
       "      <td>00:00:56.775</td>\n",
       "      <td>1.15983</td>\n",
       "      <td>1.15986</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2021.09.30</td>\n",
       "      <td>00:00:57.110</td>\n",
       "      <td>1.15982</td>\n",
       "      <td>1.15985</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2021.09.30</td>\n",
       "      <td>00:01:00.532</td>\n",
       "      <td>1.15982</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2021.09.30</td>\n",
       "      <td>00:01:01.369</td>\n",
       "      <td>1.15973</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2021.09.30</td>\n",
       "      <td>00:01:09.039</td>\n",
       "      <td>1.15978</td>\n",
       "      <td>1.15981</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       <DATE>        <TIME>    <BID>    <ASK>  <LAST>  <VOLUME>  <FLAGS>\n",
       "0  2021.09.30  00:00:00.204  1.15982  1.15985     NaN       NaN        6\n",
       "1  2021.09.30  00:00:00.409  1.15939  1.16015     NaN       NaN        6\n",
       "2  2021.09.30  00:00:00.672  1.15984  1.15987     NaN       NaN        6\n",
       "3  2021.09.30  00:00:26.007  1.15978  1.15981     NaN       NaN        6\n",
       "4  2021.09.30  00:00:27.707  1.15984  1.15987     NaN       NaN        6\n",
       "5  2021.09.30  00:00:56.775  1.15983  1.15986     NaN       NaN        6\n",
       "6  2021.09.30  00:00:57.110  1.15982  1.15985     NaN       NaN        6\n",
       "7  2021.09.30  00:01:00.532  1.15982      NaN     NaN       NaN        2\n",
       "8  2021.09.30  00:01:01.369  1.15973      NaN     NaN       NaN        2\n",
       "9  2021.09.30  00:01:09.039  1.15978  1.15981     NaN       NaN        6"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tick_df = pd.read_csv(\"EURUSD.csv\", sep=\"\\t\")\n",
    "tick_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3e347f04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(110493, 7)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tick_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00fe9852",
   "metadata": {},
   "source": [
    "## Candle dataframe\n",
    "This dataframe is obtained through the from_tick_to_candle function. The dataframe contains the following columns:\n",
    "* **TIME-UTC**: UTC-TIME since epoch in seconds.\n",
    "* **OPEN**: Open price of that candle.\n",
    "* **CLOSE**: Close price of the candle.\n",
    "* **HIGH**: Highest price of the candle.\n",
    "* **LOW**: Lowest price of the candle."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "311a20e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TIME-UTC</th>\n",
       "      <th>OPEN</th>\n",
       "      <th>CLOSE</th>\n",
       "      <th>HIGH</th>\n",
       "      <th>LOW</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.632953e+09</td>\n",
       "      <td>1.15982</td>\n",
       "      <td>1.15968</td>\n",
       "      <td>1.16015</td>\n",
       "      <td>1.15939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.632954e+09</td>\n",
       "      <td>1.15968</td>\n",
       "      <td>1.15983</td>\n",
       "      <td>1.15990</td>\n",
       "      <td>1.15964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.632955e+09</td>\n",
       "      <td>1.15983</td>\n",
       "      <td>1.15966</td>\n",
       "      <td>1.15983</td>\n",
       "      <td>1.15964</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.632956e+09</td>\n",
       "      <td>1.15966</td>\n",
       "      <td>1.15980</td>\n",
       "      <td>1.15997</td>\n",
       "      <td>1.15966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.632956e+09</td>\n",
       "      <td>1.15980</td>\n",
       "      <td>1.15975</td>\n",
       "      <td>1.15990</td>\n",
       "      <td>1.15966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.632957e+09</td>\n",
       "      <td>1.15975</td>\n",
       "      <td>1.15985</td>\n",
       "      <td>1.15992</td>\n",
       "      <td>1.15970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.632958e+09</td>\n",
       "      <td>1.15985</td>\n",
       "      <td>1.15981</td>\n",
       "      <td>1.15987</td>\n",
       "      <td>1.15979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.632959e+09</td>\n",
       "      <td>1.15981</td>\n",
       "      <td>1.15984</td>\n",
       "      <td>1.15986</td>\n",
       "      <td>1.15976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1.632960e+09</td>\n",
       "      <td>1.15984</td>\n",
       "      <td>1.15986</td>\n",
       "      <td>1.15991</td>\n",
       "      <td>1.15979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.632961e+09</td>\n",
       "      <td>1.15986</td>\n",
       "      <td>1.15999</td>\n",
       "      <td>1.16004</td>\n",
       "      <td>1.15984</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       TIME-UTC     OPEN    CLOSE     HIGH      LOW\n",
       "0  1.632953e+09  1.15982  1.15968  1.16015  1.15939\n",
       "1  1.632954e+09  1.15968  1.15983  1.15990  1.15964\n",
       "2  1.632955e+09  1.15983  1.15966  1.15983  1.15964\n",
       "3  1.632956e+09  1.15966  1.15980  1.15997  1.15966\n",
       "4  1.632956e+09  1.15980  1.15975  1.15990  1.15966\n",
       "5  1.632957e+09  1.15975  1.15985  1.15992  1.15970\n",
       "6  1.632958e+09  1.15985  1.15981  1.15987  1.15979\n",
       "7  1.632959e+09  1.15981  1.15984  1.15986  1.15976\n",
       "8  1.632960e+09  1.15984  1.15986  1.15991  1.15979\n",
       "9  1.632961e+09  1.15986  1.15999  1.16004  1.15984"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candle_df = to_candle.from_tick_to_candle(\"EURUSD.csv\", 15*60)\n",
    "candle_df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "28124df1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(183, 5)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "candle_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4689617",
   "metadata": {},
   "source": [
    "# TO_CANDLE PLOT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d0f56970",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "del candle_df['TIME-UTC']\n",
    "candle_df.plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
